Protecting Location Privacy with Personalized k-Anonymity: Architecture and Algorithms

By Bugra Gedik, Ling Liu. IEEE Transactions on Mobile Computing, 2008

943 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Electrical and Electronic Engineering, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/a2e13c752fe4a47dffcd78e158ff208ea7b5ca85

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Citations in the corpus

The pursuit of citizens' privacy: a privacy-aware smart city is possible (2013, background): “In [12] the authors propose the use of a trusted third party (TTP), which handles users' locations to create cloaking regions.”
The new Casper: query processing for location services without compromising privacy (2006, background): “, [Bamba et al. 2008; Beresford and Stajano 2003; Chow et al. 2006; Chow and Mokbel 2007; Cheng et al. 2006; Duckham and Kulik 2005; Gedik and Liu 2008; Gruteser and Grunwald 2003; Ghinita et al. 2007a; 2007b; Gruteser and Liu 2004; Hashem and Kulik 2007; Hengartner and Steenkiste 2003; Hong and Lan”
Unraveling an old cloak: k-anonymity for location privacy (2010, methodology): “Among them, k-anonymity scheme is by far the most employed protection scheme for location privacy, mostly due to its simplicity [4, 6, 12, 15, 19, 20, 21].”
Privacy Preserving Image-Based Localization (2019, positive): “Differential privacy [20] and k-anonymity [56] have been applied to the problem of location privacy [3, 6, 26, 72].”
Quantifying Location Privacy: The Case of Sporadic Location Exposure (2011, background): “[6] propose an architecture and some algorithms to protect location privacy using personalized k-anonymity.”
On sampling, anonymization, and differential privacy or, k-anonymization meets differential privacy (2011, background): “Many kanonymization methods have been developed over the last decades; it has also been extensively applied to other problems such as location privacy [14].”
Urban Sensing Using Mobile Phone Network Data: A Survey of Research (2014, background): “…obfuscation techniques; — k-anonymity for trajectories , which ensures that each individual trajectory can only be released if there are at least k − 1 distinct individuals whose associated trajectories are indistinguishable from the former (see (Gedik and Liu, 2008) for more detailed information).”
Privacy Preserving Image Queries for Camera Localization (2019, background): “Other anonymization approaches for location privacy for mobile systems have been explored [5, 6, 8, 21, 42].”
Computing with Spatial Trajectories (2011, methodology): “tasks and thus can be extended for trajectory data reduction [19, 22, 20, 21].”